Prediction of lamb body composition using in vivo bioimpedance analysis

Moro, A.B., Pires, C.C., da Silva, L.P., Dias, A.M.O., Simões, R.R., Pilecco, V.M., Mello, R.O. and de Aguiar, L. (2018) Prediction of lamb body composition using in vivo bioimpedance analysis. Meat Science, 150. pp. 1-6.

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Abstract

The objective of this study was to evaluate the potential of in vivo bioimpedance analysis (BIA) as a method to estimate body composition in lambs. Thirty-one Texel x Ile de France crossbreed ram lambs were slaughtered at pre-determined intervals of average weights of 20, 26, 32, and 38 kg. Before the slaughter of the animals, their body weight (BW) and body length (BL) were measured. The values for resistance (Rs) and reactance (Xc) were collected using a single-frequency BIA equipment (Model RJL Quantum II Bioelectrical Body Composition Analyzer). The BIA main variables such as body bioelectrical volume (V), phase angle (PA), resistive density (RsD), and reactive density (XcD) were then calculated. The soft tissue mass of the right-half cold carcass was analyzed in order to determine its chemical composition. Multiple regression analyses were performed using the lamb body composition as dependent variables and the measurements related to bioimpedance as independent variables. The best regression models were evaluated by cross-validation. The predictive model of moisture mass, which was developed by using XcD and V, accounted for 84% of its variation. Resulting models of percentage moisture (R2 = 0.79), percentage lean mass (R2 = 0.79), percentage fat (R2 = 0.79), and fat mass (R2 = 0.87) were obtained using RsD and V. Furthermore, the values of RsD regarding V, and PA in the prediction models accounted for 91% and 89% of variation in protein mass and lean mass, respectively. Bioimpedance analysis proved to be an efficient method to estimate the body composition of lambs slaughtered at different body mass stages.

Item Type: Article
Keywords: Sheep Lean mass Carcass composition, Impedance, Resistance
Divisions: Food Science and Agri-food Supply Chain Management
Depositing User: Ms Kath Osborn
Date Deposited: 08 Nov 2019 12:04
Last Modified: 03 Jan 2020 10:54
URI: http://hau.repository.guildhe.ac.uk/id/eprint/17451

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